• Anthropogenic deforestation;
  • biodiversity conservation;
  • climate change;
  • greenhouse gas emission;
  • land use change;
  • logistic regression model;
  • phcfM R package;
  • population growth;
  • REDD+


Anthropogenic deforestation in tropical countries is responsible for a significant part of global carbon dioxide emissions in the atmosphere. To plan efficient climate change mitigation programs (such as REDD+, Reducing Emissions from Deforestation and forest Degradation), reliable forecasts of deforestation and carbon dioxide emissions are necessary. Although population density has been recognized as a key factor in tropical deforestation, current methods of prediction do not allow the population explosion that is occurring in many tropical developing countries to be taken into account. Here, we propose an innovative approach using novel computational and statistical tools, including R/GRASS scripts and the new phcfM R package, to model the intensity and location of deforestation including the effect of population density. We used the model to forecast anthropogenic deforestation and carbon dioxide emissions in five large study areas in the humid and spiny-dry forests of Madagascar. Using our approach, we were able to demonstrate that the current rapid population growth in Madagascar (+3.39% per year) will significantly increase the intensity of deforestation by 2030 (up to +1.17% per year in densely populated areas). We estimated the carbon dioxide emissions associated with the loss of aboveground biomass to be of 2.24 and 0.26 tons per hectare and per year in the humid and spiny-dry forest, respectively. Our models showed better predictive ability than previous deforestation models (the figure of merit ranged from 10 to 23). We recommend this approach to reduce the uncertainty associated with deforestation forecasts. We also underline the risk of an increase in the speed of deforestation in the short term in tropical developing countries undergoing rapid population expansion.